ShakeAlert, the earthquake early warning (EEW) system for the West Coast of the United States, attempts to provides crucial warnings before strong shaking occurs. However, because the alerts are triggered only when an earthquake is already in progress, and the alert latencies and delivery times are platform dependent, the time between these warnings and the arrival of shaking is variable. The ShakeAlert system uses, among other public alerting platforms like a mobile phone operating system, smartphone apps, and the Federal Emergency Management Agency Integrated Public Alert & Warning System (IPAWS). IPAWS sends Wireless Emergency Alerts (WEAs) informing people via their smartphones and other mobile devices about various events, such as natural hazards, child abductions, or public health information about COVID-19. However, little is known about the IPAWS delivery latencies. Given that people may have only a few seconds of notice after they receive an alert to take a protective action before they feel earthquake shaking, quantifying latencies is critical to understanding whether the IPAWS system is useful for EEW. In this study, we developed new methods to test the IPAWS distribution system's performance, both with devices in a controlled environment and as well as with a 2019 community-based feedback form, in Oakland and San Diego County, California, respectively. The controlled environment test used mobile phones (including smart and non-smart phones) and associated devices to determine alert receipt times; the community research form had participants self-report their receipt times. By triangulating the data between the controlled test environment and the community research, we determined the latency statistics as well as whether the geofence (the geographic area where the alert was intended to be sent) held broadly. We found that the latencies were similar between the two tests despite the large differences in population sizes. WEA messages were received within a median time frame of 6-12 s, and the geofence held with only a few exceptions. We use this latency to assess how the system would have performed in two large earthquakes, the 1989 M6.9 Loma Prieta and 2019 M7.1 Ridgecrest earthquakes, which both occurred near our WEA test locations. Our analysis revealed that had IPAWS been available during those earthquakes, particularly Loma Prieta, it would have provided crucial seconds of notice that damaging shaking was imminent in some locations relatively far from the epicenter. Further, we find affordable non-smart phones can receive WEAs as fast as smartphones. Finally, our new method can be used for latency and geospatial testing going forward for IPAWS and other similar alerting systems.
Health and diseases are integral parts of the life of seabirds that merit attention if we expect to truly understand, protect, and conserve them. Diseases such as avian influenza, avian pox, pasteurellosis, and paralytic shellfish poisoning have a proven history of decreasing the survival or breeding success of seabirds. However, each host-pathogen-environment system is unique, and our current knowledge about seabird health is limited and subject to biases. Thus, an exploratory mindset should be maintained, always considering that new or previously undiagnosed diseases could have substantial effects on a given seabird population. Therefore, incorporating a health monitoring component in seabird population monitoring programs, wherein data and biological samples are routinely collected for long-term pathogen surveillance and physiological analyses, would help us understand factors that limit seabird populations. Finally, the implementation of biosecurity best practices at seabird aggregations is imperative to avoid the accidental introduction or spread of pathogens.
Niobium, often classified as critical, is typically embedded within steels essential for infrastructure and transportation. Most niobium-consuming countries are import-dependent on primary stage niobium, meaning traditional material flow analysis, which often excludes critical commodities embedded within products of large-scale industries, would miss important flows in the fabrication and manufacturing stages and underestimate niobium consumption. This study presents the first dynamic (2000–2020) niobium flow analysis for two niobium-consuming, net import-dependent countries: the United States (U.S.) and China. Results demonstrate that the U.S. is import-dependent throughout all stages of the niobium flow cycle including embedded and primary flows, whereas China is only import-dependent on primary niobium. Moreover, while most U.S. imports of niobium embedded within (semi-)finished goods are consumed domestically, most niobium-containing goods manufactured in China are exported, suggesting a supply disruption would affect their economies differently. This research demonstrates the necessity of embedded flows for criticality assessments and evaluating supply restrictions.
Coastal systems around the globe are being re-integrated with adjacent river systems to restore the natural hydrologic connection to riparian wetlands. The Mississippi River sediment diversions or river reconnections are one such tool to combat high rates of wetland loss in coastal Louisiana, USA by providing freshwater, sediment, and nutrients. There has been some disagreement in the published literature whether re-establishing river reconnection is slowing or contributing to coastal wetland loss. This issue is due to the difficulties in the application of remote sensing in low-relief environments where water level changes could indicate either land loss or simply temporary submergence. We analyzed land change at the receiving areas of two existing freshwater river diversions, Davis Pond and Caernarvon, which have been intermittently receiving river water for up to 2+ decades. This study provides a robust analysis of wetland land change rates in proximity these river diversions including years before river reconnection. Our analyses indicate a net land gain since river reconnection operations began at Davis Pond Diversion (+3.42 km²; range: +2.02–4.81 km²) and no statistically significant change at the Caernarvon Diversion. The Davis Pond wetland results are corroborated with data from a decadal field study documenting increased inorganic sedimentation in the soil. It is clear from this study and others, that river reconnection can increase or, in the case of Caernarvon, have no statistical effect on the land change in these systems due to differences in vegetation, hydroperiod, sediment delivery and external factors including hurricane impacts. Our remote sensing analysis was compared with a global water area change analysis mapping tool which also supported our findings.
Best management practices (BMPs) have been predominantly used throughout the Chesapeake Bay watershed (CBW) to reduce nutrients and sediments entering streams, rivers, and the bay. These practices have been successful in reducing loads entering the estuary and have shown the potential to reduce other contaminants (pesticides, hormonally active compounds, pathogens) in localized studies and modeled load estimates. However, further understanding of relationships between BMPs and non-nutrient contaminant reductions at regional scales using sampled data would be beneficial. Total estrogenic activity was measured in surface water samples collected over a decade (2008–2018) in 211 undeveloped NHDPlus V2.1 watersheds within the CBW. Bayesian hierarchical modeling between total estrogenic activity and landscape predictors including landcover, runoff, BMP intensity, and a BMP*agriculture intensity interaction term indicates a 96% posterior probability that BMP intensity on agricultural land is reducing total estrogenic activity. Additionally, watersheds with high agriculture and low BMPs had a 49% posterior probability of exceeding an effects-based threshold in aquatic organisms of 1 ng/L but only a 1% posterior probability of exceeding this threshold in high-agriculture, high-BMP watersheds.
To help better interpret computational models in predicting drift of carp eggs in rivers, we present a series of model assessments for the longitudinal egg dispersion. Two three-dimensional Lagrangian particle tracking models, SDrift and FluEgg, are evaluated in a series of channels with increasing complexity. The model evaluation demonstrates that both models are able to accommodate channel complexity and provide a wide range of dispersion coefficients: Kl=O(1−100)Hu∗ with H being water depth and u∗ being shear velocity. In a straight channel with Kl=O(1)Hu∗, SDrift predicts weaker longitudinal dispersion than FluEgg in the early stage as a result of weak vertical mixing associated with smooth wall turbulence. With sufficient time, SDrift and FluEgg predict similar egg dispersion, accounting for the differential advection due to the vertical velocity profile. In an idealized curved channel with Kl=O(10)Hu∗, dispersion is driven by both vertical and transverse velocity profiles. SDrift yields slightly larger dispersion coefficients than FluEgg. In a real river with channel-training structures and having Kl=O(100)Hu∗, SDrift predicts a stronger longitudinal dispersion than FluEgg due to substantial local turbulent eddies and velocity gradients. To summarize, FluEgg shows good performance in capturing dispersion due to vertical velocity profiles and cross-channel velocity gradients. SDrift shows excellent model capabilities of revealing various dispersion mechanisms in addition to the vertical and cross-channel velocity variations. They include the initial turbulent diffusion stage with growing dispersion coefficients and strong dispersion due to in-stream hydraulic structures and localized turbulence.
Background The COVID-19 Scenario Modeling Hub convened nine modeling teams to project the impact of expanding SARS-CoV-2 vaccination to children aged 5–11 years on COVID-19 burden and resilience against variant strains. Methods Teams contributed state- and national-level weekly projections of cases, hospitalizations, and deaths in the United States from September 12, 2021 to March 12, 2022. Four scenarios covered all combinations of 1) vaccination (or not) of children aged 5–11 years (starting November 1, 2021), and 2) emergence (or not) of a variant more transmissible than the Delta variant (emerging November 15, 2021). Individual team projections were linearly pooled. The effect of childhood vaccination on overall and age-specific outcomes was estimated using meta-analyses. Findings Assuming that a new variant would not emerge, all-age COVID-19 outcomes were projected to decrease nationally through mid-March 2022. In this setting, vaccination of children 5–11 years old was associated with reductions in projections for all-age cumulative cases (7.2%, mean incidence ratio [IR] 0.928, 95% confidence interval [CI] 0.880–0.977), hospitalizations (8.7%, mean IR 0.913, 95% CI 0.834–0.992), and deaths (9.2%, mean IR 0.908, 95% CI 0.797–1.020) compared with scenarios without childhood vaccination. Vaccine benefits increased for scenarios including a hypothesized more transmissible variant, assuming similar vaccine effectiveness. Projected relative reductions in cumulative outcomes were larger for children than for the entire population. State-level variation was observed. Interpretation Given the scenario assumptions (defined before the emergence of Omicron), expanding vaccination to children 5–11 years old would provide measurable direct benefits, as well as indirect benefits to the all-age U.S. population, including resilience to more transmissible variants. Funding Various (see acknowledgments).
Volcano slope stability analysis is a critical component of volcanic hazard assessments and monitoring. However, traditional methods for assessing rock strength require physical samples of rock which may be difficult to obtain or characterize in bulk. Here, visible to shortwave infrared (350–2500 nm; VNIR–SWIR) reflected light spectroscopy on laboratory-tested rock samples from Ruapehu, Ohakuri, Whakaari, and Banks Peninsula (New Zealand), Merapi (Indonesia), Chaos Crags (USA), Styrian Basin (Austria) and La Soufrière de Guadeloupe (Eastern Caribbean) volcanoes was used to design a novel rapid chemometric-based method to estimate uniaxial compressive strength (UCS) and porosity. Our Partial Least Squares Regression models return moderate accuracies for both UCS and porosity, with R² of 0.43–0.49 and Mean Absolute Percentage Error (MAPE) of 0.2–0.4. When laboratory-measured porosity is included with spectral data, UCS prediction reaches an R² of 0.82 and MAPE of 0.11. Our models highlight that the observed changes in the UCS are coupled with subtle mineralogical changes due to hydrothermal alteration at wavelengths of 360–438, 532–597, 1405–1455, 2179–2272, 2332–2386, and 2460–2490 nm. These mineralogical changes include mineral replacement, precipitation hydrothermal alteration processes which impact the strength of volcanic rocks, such as mineral replacement, precipitation, and/or silicification. Our approach highlights that spectroscopy can provide a first order assessment of rock strength and/or porosity or be used to complement laboratory porosity-based predictive models. VNIR-SWIR spectroscopy therefore provides an accurate non-destructive way of assessing rock strength and alteration mineralogy, even from remote sensing platforms.
Most wildfires are started by humans, however, geographic variation of potential ignition sources is not often explicitly accounted for in wildfire simulation modelling or risk assessments. In this study, we investigated how patterns of human and lightning ignitions can influence modelled fire simulations and demonstrate how these data can be used to assess post-fire flooding and sediment transport.Weusedhistoricalignitiondata(1992–2015) to characterize ignition patterns for thirteen mountain ranges in southern Arizona, United States, and developed FlamMap burn probability (BP) models for three scenarios: human ignition, lightning ignition, and random ignition. We then developed a watershed-scale case study assessing the impacts of ignition scenarios on post-fire hydrology using the KINEROS2 model that simulates runoff and erosion. BP models illustrated considerable differences in landscape fire risk between the three ignition scenarios. Results from the watershed model indicate the greatest impacts from the post-fire human ignition scenario, with a 10-fold increase in sediment discharge and four-fold increase in peak flow compared to pre-fire conditions. Our results show that consideration of ignition source and location is important for assessing fire risk, and our modelling approach provides a planning mechanism to identify locations most at risk to fire-induced flood hazards, where prevention and mitigation activities can be focused.
While nonstationary flood frequency analysis (NSFFA) methods have proliferated, few studies have rigorously compared them for modeling changes in both the central tendency and variability of annual maximum series (AMS) in hydrologically diverse areas. Through Monte Carlo experiments, we appraise five methods for updating 10- and 100-year floods at gauged sites using synthetic records based on sample moments and change trajectories of observed AMS in the conterminous United States (CONUS). We compare two methods that consider changes in both central tendency and variability - a Gamma generalized linear model estimated with weighted least squares and the Generalized Additive Model for Location, Scale, Shape (GAMLSS) - with a distribution-free approach (quantile regression), and baseline cases assuming stationarity or only changes in central tendency. ‘Trend-space’ plots identify realistic AMS changes for which modeling trends in both central tendency and variability were warranted based on fractional root mean squared errors (fRMSE). They also reveal statistical properties of AMS under which NSFFA models perform especially well or poorly. For instance, quantile regression performed especially well (poorly) under strong negative (positive) skewness. Although the nonstationary LP3 distribution accommodates most AMS with trends well, the sensitivity of NSFFA model performance to different sample moments and trends suggests the need for more flexibility in prescribing design-flood adjustments in CONUS. A follow-up comparison of regional NSFFA models pooling at-site AMS would further illuminate NSFFA guidance, especially for AMS with properties less conducive to NSFFA modeling, such as positive skewness and increasing variability.
Extreme precipitation events may cause flooding, slope failure, erosion, deposition, and damage to infrastructure over a regional scale, but the impacts of these events are often difficult to fully characterize. Regional‐scale landscape change occurred during an extreme rain event in June 2012 in northeastern Minnesota. Landscape change was documented by 8,000 km² of airborne lidar data collected before and after the event. Following improved alignment of the lidar point data and reducing error using insight from analysis of extensive stable areas, elevation differences were classified into map objects representing geomorphic change in relation to process and landscape position using object‐based image analysis. This remote mapping compares favorably to field and imagery‐based mapping and provides the basis for volumetric sediment budgeting. Elevation differences in these objects indicate that 4.5 × 10⁶ ± 1.0 × 10⁶ m³ of sediment was eroded in the study area. Of this, 2.5 × 10⁶ ± 3.3 × 10⁵ m³ was deposited in deposits on hillslopes and valley floors, and 2.0 × 10⁶ ± 4.6 × 10⁵ m³ were removed from watersheds and exported to the Saint Louis River Estuary and Lake Superior. Multivariate logistic regression analysis emphasized that topographic slope and presence of glaciolacustrine clay lithology are the primary control on landslide occurrence, and landslides occur most frequently on slopes within tens of meters of stream channels. These results provide the basis to anticipate the impacts of similar future storm events. Because precipitation events are forecast to continue to increase in frequency and intensity owing to climate change, characterizing and anticipating their effects may support hazard planning.
The North Australian Zinc Belt is the largest zinc-lead province in the world, containing three of the ten largest known individual deposits (HYC, Hilton-George Fisher, and Mount Isa). The Northern Cordillera in North America is the second largest zinc-lead province, containing a further two of the world’s top ten deposits (Red Dog and Howards Pass). Despite this world-class endowment, exploration in both mineral provinces during the past 2 decades has not been particularly successful, yielding only two significant discoveries (Teena, Australia, and Boundary, Canada). One of the most important aspects of exploration is to choose mineral provinces and districts within geological belts that have the greatest potential for discovery. Here, we present results from these two zinc belts that highlight previously unused datasets for area selection and targeting. Lead isotope mapping using analyses of mineralized material has identified gradients in μ ( ²³⁸ U/ ²⁰⁴ Pb) that coincide closely with many major deposits. Locations of these deposits also coincide with a gradient in the depth of the lithosphere-asthenosphere boundary determined from calibrated surface wave tomography models converted to temperature. Furthermore, gradients in upward-continued gravity anomalies and a step in Moho depth correspond to a pre-existing major crustal boundary in both zinc belts. A spatial association of deposits with a linear mid- to lower-crustal resistivity anomaly from magnetotelluric data is also observed in the North Australian Zinc Belt. The change from thicker to thinner lithosphere is interpreted to localize prospective basins for zinc-lead mineralization and to control the gradient in lead isotope and geophysical data. These data, when combined with data indicative of paleoenvironment and changes in plate motion at the time of mineralization, provide new exploration criteria that can be used to identify prospective mineralized basins and define the most favorable parts of these basins.
The Amazon biome is being pushed by unsustainable economic drivers towards an ecological tipping point where restoration to its previous state may no longer be possible. This degradation is the result of self-reinforcing interactions between deforestation, climate change and fire. We assess the economic, natural capital and ecosystem services impacts and trade-offs of scenarios representing movement towards an Amazon tipping point and strategies to avert one using the Integrated Economic-Environmental Modeling (IEEM) Platform linked with spatial land use-land cover change and ecosystem services modeling (IEEM+ESM). Our approach provides the first approximation of the economic, natural capital and ecosystem services impacts of a tipping point, and evidence to build the economic case for strategies to avert it. For the five Amazon focal countries, namely, Brazil, Peru, Colombia, Bolivia and Ecuador, we find that a tipping point would create economic losses of US$256.6 billion in cumulative Gross Domestic Product by 2050. Policies that would contribute to averting a tipping point, including strongly reducing deforestation, investing in intensifying agriculture in cleared lands, climate-adapted agriculture and improving fire management, would generate approximately US$339.3 billion in additional wealth and a return on investment of US$29.5 billion. Quantifying the costs, benefits and trade-offs of policies to avert a tipping point in a transparent and replicable manner can support the design of regional development strategies for the Amazon biome, build the business case for action and catalyze global cooperation and financing to enable policy implementation.
Laterally directed explosive eruptions are responsible for multiple fatalities over the past decade and are an increasingly important volcanology problem. To understand the energy dynamics for these events, we collected field-scale explosion data from nine acoustic sensors surrounding a tiltable cannon as part of an exploratory experimental design. For each cannon discharge, the blast direction was varied systematically at 0°, 12°, and 24° from vertical, capturing acoustic wavefield directivity related to the tilt angle. While each event was similar in energy discharge potential, the resulting acoustic signal features were variable event-to-event, producing non-repetitious waveforms and spectra. Systematic features were observed in a subset of individual events for vertical and lateral discharges. For vertical discharges, the acoustic energy had a uniform radiation pattern. The lateral discharges showed an asymmetric radiation pattern with higher frequencies in the direction of the blast and depletion of those frequencies behind the cannon. Results suggest that, in natural volcanic systems, near-field blast directionality may be elucidated from acoustic sensors in absence of visual data, with implications for volcano monitoring and hazard assessment. Graphical Abstract
The Chesapeake Bay is a region along the eastern coast of the United States where sea-level rise is confounded with poorly resolved rates of land subsidence, thus new constraints on vertical land motions (VLM) in the region are warranted. In this paper, we provide a description of two campaign-style Global Positioning System (GPS) datasets, explain the methods used in data collection and validation, and present the experiment designed to quantify a new baseline of VLM in the Chesapeake Bay region of eastern North America. Data from GPS campaigns in 2019 and 2020 are presented as ASCII RINEX2.11 files and logsheets for each observation from the campaigns. Data were quality checked using the open-source program TEQC, resulting in average multipath 1 and 2 values of 0.68 and 0.57, respectively. All data are archived and publicly available for open access at the geodesy facility UNAVCO to abide by Findable, Accessible, Interoperable, Reusable (FAIR) data principles. Measurement(s)motion of the Earth’s surfaceTechnology Type(s)Global Positioning SystemSample Characteristic - EnvironmentChesapeake BaySample Characteristic - LocationUnited States Measurement(s) motion of the Earth’s surface Technology Type(s) Global Positioning System Sample Characteristic - Environment Chesapeake Bay Sample Characteristic - Location United States
Background China has committed to achieving peak CO2 emissions before 2030 and carbon neutrality before 2060; therefore, accelerated efforts are needed to better understand carbon accounting in industry and energy fields as well as terrestrial ecosystems. The carbon sink capacity of plantation forests contributes to the mitigation of climate change. Plantation forests throughout the world are intensively managed, and there is an urgent need to evaluate the effects of such management on long-term carbon dynamics. Methods We assessed the carbon cycling patterns of ecosystems characterized by three typical plantation species (Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.), oak (Cyclobalanopsis glauca (Thunb.) Oerst.), and pine (Pinus massoniana Lamb.)) in Lishui, southern China, by using an integrated biosphere simulator (IBIS) tuned with localized parameters. Then, we used the state-and-transition simulation model (STSM) to study the effects of active forest management (AFM) on carbon storage by combining forest disturbance history and carbon cycle regimes. Results 1) The carbon stock of the oak plantation was lower at an early age (<50 years) but higher at an advanced age (>50 years) than that of the Chinese fir and pine plantations. 2) The carbon densities of the pine and Chinese fir plantations peaked at 70 years (223.36 Mg·ha‒1) and 64 years (232.04 Mg·ha‒1), respectively, while the carbon density in the oak plantation continued increasing (>100 years). 3) From 1989 to 2019, the total carbon pools of the three plantation ecosystems followed an upward trend (an annual increase of 0.16–0.22 Tg C), with the largest proportional increase in the aboveground biomass carbon pool. 4) AFM increased the recovery of carbon storage after 1996 and 2009 in the pine and Chinese fir plantations, respectively, but did not result in higher growth in the oak plantation. 5) The proposed harvest planning is reasonable and conducive to maximizing the carbon sequestration capacity of the forest. Conclusions This study provides an example of a carbon cycle coupling model that is potentially suitable for simulating China's plantation forest ecosystems and supporting carbon accounting to monitor peak CO2 emissions and reach carbon neutrality.
Background Deep-sea mussels in the subfamily Bathymodiolinae have unique adaptations to colonize hydrothermal-vent and cold-seep environments throughout the world ocean. These invertebrates function as important ecosystem engineers, creating heterogeneous habitat and promoting biodiversity in the deep sea. Despite their ecological significance, efforts to assess the diversity and connectivity of this group are extremely limited. Here, we present the first genomic-scale diversity assessments of the recently discovered bathymodioline cold-seep communities along the U.S. Atlantic margin, dominated by Gigantidas childressi and Bathymodiolus heckerae . Results A Restriction-site Associated DNA Sequencing (RADSeq) approach was used on 177 bathymodiolines to examine genetic diversity and population structure within and between seep sites. Assessments of genetic differentiation using single-nucleotide polymorphism (SNP) data revealed high gene flow among sites, with the shallower and more northern sites serving as source populations for deeper occurring G. childressi . No evidence was found for genetic diversification across depth in G. childressi , likely due to their high dispersal capabilities. Kinship analyses indicated a high degree of relatedness among individuals, and at least 10–20% of local recruits within a particular site. We also discovered candidate adaptive loci in G. childressi and B. heckerae that suggest differences in developmental processes and depth-related and metabolic adaptations to chemosynthetic environments. Conclusions These results highlight putative source communities for an important ecosystem engineer in the deep sea that may be considered in future conservation efforts. Our results also provide clues into species-specific adaptations that enable survival and potential speciation within chemosynthetic ecosystems.
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